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        "%matplotlib inline"
      ]
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    {
      "cell_type": "markdown",
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      "source": [
        "\n# Isotonic Regression\n\n\nAn illustration of the isotonic regression on generated data. The\nisotonic regression finds a non-decreasing approximation of a function\nwhile minimizing the mean squared error on the training data. The benefit\nof such a model is that it does not assume any form for the target\nfunction such as linearity. For comparison a linear regression is also\npresented.\n\n\n"
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    {
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      "execution_count": null,
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      "source": [
        "print(__doc__)\n\n# Author: Nelle Varoquaux <nelle.varoquaux@gmail.com>\n#         Alexandre Gramfort <alexandre.gramfort@inria.fr>\n# License: BSD\n\nimport numpy as np\nimport matplotlib.pyplot as plt\nfrom matplotlib.collections import LineCollection\n\nfrom sklearn.linear_model import LinearRegression\nfrom sklearn.isotonic import IsotonicRegression\nfrom sklearn.utils import check_random_state\n\nn = 100\nx = np.arange(n)\nrs = check_random_state(0)\ny = rs.randint(-50, 50, size=(n,)) + 50. * np.log1p(np.arange(n))\n\n# #############################################################################\n# Fit IsotonicRegression and LinearRegression models\n\nir = IsotonicRegression()\n\ny_ = ir.fit_transform(x, y)\n\nlr = LinearRegression()\nlr.fit(x[:, np.newaxis], y)  # x needs to be 2d for LinearRegression\n\n# #############################################################################\n# Plot result\n\nsegments = [[[i, y[i]], [i, y_[i]]] for i in range(n)]\nlc = LineCollection(segments, zorder=0)\nlc.set_array(np.ones(len(y)))\nlc.set_linewidths(np.full(n, 0.5))\n\nfig = plt.figure()\nplt.plot(x, y, 'r.', markersize=12)\nplt.plot(x, y_, 'b.-', markersize=12)\nplt.plot(x, lr.predict(x[:, np.newaxis]), 'b-')\nplt.gca().add_collection(lc)\nplt.legend(('Data', 'Isotonic Fit', 'Linear Fit'), loc='lower right')\nplt.title('Isotonic regression')\nplt.show()"
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